Faculty of mathematics and computer science

REFERENCES

[ALE95] Allen, J., Natural language understanding, Benjamin/Cummings Publ.,

2^nd ed., 1195

[ANT96] Antos, A., Lugosi, A., Strong MiniMax lower bounds for learning, Proceedings of the 9^th annual Conference on Computational Learning theory, 1996, pp.303-309

[BAR91] Barto, A.G., Bradtke, S.J., Singh, S.P., Real time learning and control using asynchronous dynamic programming, (Technical Report 91-57), Amherst, MA: University of Massachusetts, Computer Science Department

[BAR99] Barber, K.S., Kim, J., Constructing and dynamically maintaining perspective-based agent models in a MA Environment, Proceedings of the 3^rd annual Conference on Autonomous Agents, 1999, pp.416-417

[BER91] Bergadano, F., Giordana, A., Machine Learning, Ellis Horwood Limited, Chichester, England, 1991

[BET93] Betke, M., Rivest, R., Singh, M., Piecemeal Learning of an Unknown Environment, AI Memo, No. 1474, CBCL Memo No. 93, 1993

[BOO88] Booker, L., B. Classifier systems that learn world models, Machine Learning, Number 3, 1988, pp.161-192

[BOO94] Booch, G., Object-Oriented Analysis and Design, Addison-Wesley: Reading, MA, 1994

[BRA82] Brady, M., Hollerbach, J.M., Robot motion: planning and control, Cambridge MA, MIT Press, 1982

[BR086] Brooks, R.A,, A robot layered control system for a mobile robot, IEE Journal of Robotics and Automation, 2(1), pp.14-23, 1986

[CAN88] Canny, J.F., The complexity of robot motion planning, Cambridge MA, MIT Press, 1988

[CAR79] Carthy, J., Ascribing mental qualities to machines, Philosophical Perspectives in Artificial Intelligence, Harvester Press, 1979

[CÂR94] Cârstoiu, D. Sisteme expert, Editura ALL, Bucureşti, 1994

[CAS94] Cassandra, A.R., Kaelbling, L.P., Littman, M.L., Acting Optimally in Partial Observable Stochastic Domains, Proceedings, 7^th AAAI-94, Seattle, Washington, 1994

[CHA95] Chaib-Draa, B., Industrial Applications of Distributed AI, Communications on the ACM, Vol. 38, No.11, 26 Octombrie, 1997

[CHA96] Charniak, E. Statistical language learning, The MIT Press, 1996

[CHE80] Chellas, B., Modal Logic: An Introduction, Cambridge University Press: Cambridge, England, 1980

[CHI99] Chiampolini, A., Laerma, E., Mello, P., Abductive Coordination for logic agents, Proceedings of the 1999 ACM symposium on Applied computing, 1999, pp.134-140

[COB02] http://titania.cobuild.collins.co.uk/form.html

[DAE00] Daelemans, W., Zavrel, J., Tilburg Memory Learner - version 3.0, University of Antwerp, http://ilk.kub.nl, 2000

[DAY92] Dayan, P., The convergence of TD() for general , Machine Learning, Number 8, 1992, pp.341-362

[DEN87] Dennett, D.C., The Intentional Stance, The MIT Press: Cambridge. MA, 1987

[DUM95] Dumitrescu, D., Inteligenţă Artificială, Litografie, Universitatea "Babeş-Bolyai" Cluj-Napoca, 1995

[DUM99] Dumitrescu, D., Principiile Inteligenţei Artificiale, Ed. Albastră, Cluj-Napoca, 1999

[ESC00] Escudero, G., Marquez, L., Rigau, G. Boosting applied to WSD, ACML, Barcel ona, Spain , 2000

[ETZ96] Etzioni, O., Moving up the information food chain: Deploying softbots on the world-wide web, Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), Portland. OR, 1996

[FIE94] Fiechter, C.N., Efficient Reinforcement Learning, Proceedings of the 7^th annual ACM Conference on Computer Learning theory, 1994, pp.88-97

[FIS93] Fisher, M., Concurrent MetateM, A language for modeling reactive systems, Proceedings of Parallel Architectures and languages Europe (PARLE), Springer Verlag, 1993

[FIS93] Fisher, M., Wooldrige, M., Specifying and Verifying Distributed Intelligent Systems, in Progress in Artificial Intelligence, Proceedings of Portuguese Conference on Artificial Intelligence (EPIA), Porto, Portugal, 1993, Springer Verlag as "Lecture notes in Computer Science", vol. 727

[FIS95/1] Fisher, M., Towards a semantics for Concurrent MetateM, in Executable Modal and Temporal Logics, Springer Verlag, Lecture Notes in Artificial Intelligence, vol. 897, 1995

[FIS95/2] Fisher, M., Representing and Executing Agent-based Systems, Springer Verlag, Lecture Notes in Computer Science, vol. 890, 1995

[FLA94] Flach, P., Simply Logical Intelligent reasoning by Example, John Wiley & Sons, Chichester, England, 1994

[FLO94] Florea, A., Boangiu, A., Inteligenţă Artificială, Editura Universităţii "Politehnica", Bucureşti, 1994

[FRE94] Frenţiu, M., Pârv, B., Elaborarea Programelor. Metode şi tehnici moderne, Editura Pro-Media, Cluj-Napoca, 1994

[GAR95] Gârbacea, I., Andonie, R., Algoritmi fundamentali – O perspectiva C++, Ed. Libris, Cluj-Napoca, 1995

[GEN94] Genesereth, M.R., Ketchpel, S.P., Software agents, Communications of the ACM, 37(7), 1994 , pp.48-53

[GRE90] Grefenstette, J.J., Ramsey, C.L., Schultz, A.C., Learning sequential decision rules using simulation models and competition, Machine Learning, Number 5, 1990, pp.355-382

[GRE93] Grefenstette, J., Genetic Algorithms and Machine Learning, Proceedings of the 6^th annual ACM Conference on Computational Learning theory, 1993, pp.3-4

[HAD96] Haddadi, A., Communication and Cooperation in Agent Systems, LNAI Volume 1056, Springer-Verlag: Berlin, Germany, 1996

[HAY83] Hayes-Roth, F., Waterman, A., Building Expert Systems, Addison-Wesley: Reading, MA, 1983

[HAR00] Harmon, M., Harmon, S., Reinforcement Learning – A Tutorial, Wright State University, www-anw.cs.umass.edu./~mharmon/rltutorial/frames. html, 2000

[HAR85] Harmon, P., King, D., Expert systems, John Wiley&Sons Inc, 1985

[HAR88] Harmon, P., Maus, R., Expert systems. Tools and applications, John Wiley&Sons Inc, 1988

[HAS88] Hassler, D., Artificiall Intelligence, Vol. 106, Number 2, 1988

[HOL75] Holland, J.H., Adaptation in natural and artificial systems, Ann Arbor, MI, University of Michigan Press, 1975

[HOL86] Holland, J.H., Escaping brittleness: The possibilities of general-purpose learning algorithms applied to parallel rule-based systems, in Machine Learning, An Artificial Intelligence Approach, Volume 2, Los Altos, CA: Morgan Kaufman, R.S. Michalski, J.G. Carbonelli, T.M. Mitchell (Eds.) 1986, pp.593-623

[ISH95] Ishida, T., Korf, R.E., A moving target search. A real-time search for changing goals. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1995

[JAC86] Jackson, P., Introduction to Expert Systems, Addison-Wesley: Reading, MA, 1986

[JUN97] Junling, H., Wellman, M., Learning about other agents in a dynamic multi-agent system, Artificial Intelligence Laboratory, Michigan, 26 Octombrie, 1997

[JUN98] Junling, H., Wellman, M., Online learning about other agents in a dynamic MAS, Proceedings of the 2^nd International Conference on Autonomous Agents, 1998, pp.239-246

[JUR00] Jurafsky, D., Martin, J.H., Speech and language processing, Prentice Hall, 2000

[KEL93] Kelly, J., Artificial Intelligence - a modern myth, Ellis Horwood, 1993

[KIL01] Kilgarriff, A., Gold Standard Datasets for Evaluating Word Sense Disambiguation Programs, http:// citeseer.nj.nec.com

[KOR90] Korf, R.E., Real-time heuristic search, Artificial Intelligence, 1990

[KOR92] Korf, R.E., Search, Encyclopedia of Artificial Intelligence, Wiley-Interscience Publication, New York, 1992

[LEE88] Lee, K.F., Majahan, S., A pattern classification approach to evaluation function learning, in Artificial Intelligence, 36, 1988, pp.1-25

[LES96] Lesperance, Y., Levesque, J., Foundation of a logical approach to agent programming, LNAI Volume 1037, Springer Verlag: Berlin, Germany, 1996, pp.331-346

[LES99] Lesser, V., Atighetchi, M., The UMASS Intelligent home project, Proceedings of the 3^rd annual Conference on Autonomous Agents, 1999, pp.291-298

[LET99] Leţia, I.A, Crăciun, F., şi alţii, Multiagent Systems, Casa Cărţii de Ştiinţă, Cluj-Napoca, 1999

[LIN91/1] Lin, L.J., Self-improving reactive agents: Case studies of reinforcement learning frameworks, Proceedings of the 1^st International Conference on Simulation of Adaptive Behavior: From Animals to Animats, 1991, pp.297-305

[LIN91/2] Lin, L.J., Self-improvement based on reinforcement learning, planning and teaching, Proceedings of the 8^th International Workshop on Machine Learning, 1991, pp.323-327

[LIN91/1] Lin, L.J., Programming robots using reinforcement learning and teaching, Proceedings of AAAI-91, 1991, pp.781-786

[MAE90] Maes, P., Situated agents can have goals, in Designing Autonomous Agents, the MIT Press: Cambridge, MA, 1990, pp.49-70

[MAH90] Mahadevan, S., Connell, J., Automatic programming of behavior-based robots using reinforcement learning, IBM Technical Report, 1990

[MAN99] Manning, C., Schutze, H., Foundation of statistical natural language
processing, MIT, 1999

[MAT94] Mataric, J.M., Interaction and Intelligent Behavior, PhD Thesis, Massachusetts Institute of Technology, 1994

[MIN61] Minsky, M.L., Computers and Thought, Mc Graw Hill, New York, 1963, E.A. Feigenbaum & J. Feldman (Eds), pp.406-450

[MIT98] Mitchell, M., An introduction to Genetic Algorithms, MIT Press, Cambridge 1998

[MOR99] Morley, D., Semantics of Actions, Agents and Environments, Doctoral Thesis, University of Melbourne, 1999

[NAR98] Nardi, B.A., Miller, J., Wright, D., Collaborative programmable Intelligent Agents, Communications ACM 41, 3, March, 1998, pp.96-104

[NEW82] Newell, A., The Knowledge Level, in Artificial Intelligence 18(1), 1982, pp.87-127

[NIL80] Nilsson, N.J., Principles of Artificial Intelligence, Palo Alto, C.A. Morgan-Kaufmann, 1980

[NIL98] Nilsson, N.J., Artificial Intelligence - A new Synthesis, Morgan-Kaufmann Publishers Inc., San Francisco, California, 1998

[PER98] Perez-Uribe, A., Sanchez, E., BlackJack as a TestBed for Learning Strategies in Neural Networks, Proceedings of the IEEE International Joint Conference on Neural Networks, 1998

[POO98] Poole, D., MachWorth, A., Goebel, R., Computational Intelligence - A Logical Approach, New York, Oxford University Press, 1998

[PYE99] Pyeatt, D.L, Howe, A.E., Integrated POMDP and Reinforcement Learning for a two Layer simulated robot architecture, Proceedings of the 3^rd annual Conference on Autonomous Agents, 19989, pp.168-174

[RAO92] Rao, A.S., Georgeff, M., An abstract architecture for rational agents, Proceedings of Knowledge Representation and Reasoning (KR&R-92), 1992, pp.439-449

[RAO95] Rao, A.S., Georgeff, M., BDI Agents: from theory to practice, Proceedings of the First International Conference on Multi-Agent Systems (ICMAS-95), pp.312-319, San Francisco, CA, 1995

[RES98] Resnik, P., Yarowsky, D., Distinguishing Systems and Distinguishing
sense: new evaluation methods fot WSD, Natural Language Engineering, 1, nr. 1, 1998

[RIC91] Rich, E., Knight, K., Artificial Intelligence, Mc Graw Hill, New York, 1991, 2^nd ed.

[RUS95] Russell, J.S, Norvig, P., Artificial Intelligence- A Modern Approach, Prentice- Hall, Inc.,New Jersey, 1995

[RUS98] Russell, J.S., Learning agents for uncertain environments, Proceedings of the 11^th annual Conference on Computational Learning theory, 1998, pp.101-103

[SAM59] Samuel, A.L., Some studies in Machine Learning using the game of checkers, IBM Journal on research and Development, Number 3, 1959, pp.210-229

[SEB01] Sebastiani, F., A tutorial on Automated Text Categorization, pp.1-25,

[SEO00] Seo, Y.W., Byoung, T.Z., A Reinforcement Learning agent for personalized information filtering, Proceedings of the 2000 International Conference on Intelligent user Interfaces, 2000, pp.248-251

[SHI98] Shimbo, M., Ishida, T., On the convergence of real-time search.. Journal of Japanese Society of Artificial Intelligence, 1998

[SHO93] Shoham, Y., Agent-oriented programming, Artificial Intelligence, 60(1), pp.51-92, 1993

[SUN96] Sung, K.K., Niyogi, P., A formulation for active learning with application to Object Detection, AI Memo No. 1438, CBCL Paper No. 116, 1996

[SUN99] Sun, R., Session, C., Bidding in Reinforcement Learning - a paradigm for MAS, Proceedings of the 3^rd annual Conference on Autonomous Agents, 1999, pp.344-345

[SUT84] Sutton, R., Temporal credit assignment in Reinforcement Learning, Doctoral Dissertation, Dept. of Computer and Information Science, Univ. of Massachusetts, Amherst, 1884

[SUT88] Sutton, R., Learning to predict by the methods of temporal differences, in Machine Learning, 3, 1988, pp.9-44

[SUT92] Sutton, R., A Special Issue of Machine Learning on Reinforcement learning, Kluwer Academy Publishers, Boston, London, 1992

[SUT98] Sutton, R.S., Barto, A.G., Reinforcement learning, The MIT Press Cambridge, Massachusetts, London, England, 1998

[TAT01] Tătar, D., Inteligenţa artificială: demonstrare automată de teoreme, pre-lucrarea limbajului natural, Editura Albastră, 2000

[TOR90] Torras, C., Illari, J., 2D path planning: A configuration space heuristic approach, The International Journal of Robotics Research, Number 2, 1990, pp.75-91

[WAL65] Waltz, M.D., Fu, K.S., A heuristic approach to reinforcement learning control systems, IEEE Transactions on Automatic Control, AC-10, 1965, pp.390-398

[WAN99] Wang, F., McKenzie, E., A multi-agent based evolutionary artificial Neural Network for general navigation in unknown environments, Proceedings of the 3^rd annual Conference on Autonomous Agents, 1999, pp.154-159

[WAT97] Watson, M., Intelligent Java applications for Internet and intranets, 1997, traducere, Ed. ALL, 1999

[WAT89] Watkins, C.J., Learning from delayed rewards, Ph.D. Thesis, King's College, Cambridge, 1989

[WEI99] Weiss, G., Multiagent systems – A Modern Approach to Distributed Artificial Intelligence, The MIT Press, Cambridge, Massachusetts, London, 1999

[WER87] Werbos, P.J., Building and understanding adaptive systems: A statistical/numerical approach to factory automation and brain research, IEEE Transactions on Systems, Man and Cybernetics, Jan-Feb, 1987

[WHI89] Whitehead, S.D., Ballard, D.H., A role for anticipation in reactive systems that learn, Proceedings of the 6^th International Workshop on Machine Learning, 1989, pp.354-357

[WHI90] Whitehead, S.D., Ballard, D.H., Active perception and Reinforcement Learning, Proceedings, 7^th International Conference on machine Learning, Austin, Texas, 1990

[WHI91] Whitehead, S.D., Ballard, D.H., Learning to perceive and act by trial and error, Machine Learning, Number 7, 1991, pp.45-84

[WIN84] Winston, P.H., Artificial Intelligence, Addison Wesley, Reading, MA, 1984, 2^nd ed.

[WOL99] Wolpert, D., Wheeler, K., Turner, K., General Principles of learning-based MAS, Proceedings of the 3^rd annual Conference on Autonomous Agents, 1999, pp.77-83

[WOO95] Wooldridge, M., Jennings, N.R., Intelligent Agents. Theory and practice., The Knowledge Engineering Review, 10(2), pp. 115-152, 1995

[WOO97] Wooldridge, M., Agent-Based Software Engineering, Mitsubishi Electric Digital Library Group, London, 1997

[WOR01] http://www.cogsci.princeton.edu/~wn/

[YAR95] Yarowsky, D., Unsupervised Word Sense Disambiguation Rivaling Supervised Methods, Proceedings of ACL'95, pp.189-196

[YAR99] Yarowsky, D., Hierarchical Decision Lists for WSD, Kluwer Acadmic Publishers, 1999

[YAR01] Yarowsky, D., Word­Sense Disambiguation Using Statistical Models of Roget's Categories Trained on Large Corpora, http:// citeseer.nj.nec.com